5 Posteriors

5.1 Plotting Code

We generate density plots in their most basic forms without post-processing.

5.2 Well Mass Flow

g1 = ggplot(outframe %>% 
              filter(facility %in% well_names, variable=="mf_pred", value>0) %>%
              mutate(source = ifelse(facility %in% dry_wells, "PI time series", "Production curve")), 
            aes(x=value, fill=facility)) +
  geom_density(aes(y=..scaled..), alpha=0.5, color=NA) + xlim(0, NA) +
  facet_grid(source~.) +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank()) +
  labs(title=paste("Posterior Well Mass Flows for", prediction_date), x="Mass flow (T/h)", y="Scaled density", fill="Facility")# +
  # ggsave('../_media/mf_wells.png', width=24.7, height=8, units='cm')
ggplotly(g1, tooltip=c('facility', 'value'))

5.3 Decline Rate

g2 = ggplot(outframe %>% filter(variable=="beta_date", facility %in% special_wells), aes(x=value, fill=facility)) +
  geom_density(alpha=0.5, color=NA) +labs(title="Posterior Decline Rate of Test Data", x="beta_date (T/h/Bar)", y="Density", fill="Facility") +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank())# +
  # ggsave('../_media/beta_date.png', width=24.7, height=6, units='cm')
ggplotly(g2, tooltip=c('facility', 'value'))

5.4 FP Mass Flow

g4 = ggplot(outframe %>% filter(facility %in% gen_names, variable=="mf_pred", value>0), aes(x=value, fill=facility)) +
  geom_density(aes(y=..scaled..), alpha=0.5, color=NA) + xlim(0, NA) + 
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank()) +
  labs(title=paste("Posterior Generator Values for", prediction_date), x="Mass flow (T/h)", y="Scaled density", fill="Facility")# +
  # ggsave('../_media/mf_gens.png', width=24.7, height=10, units='cm')
ggplotly(g4, tooltip=c('facility', 'value'))

5.5 Gen Mass Flow

g5.actual = data.frame(facility = c("WRK", "THI", "POI", "BIN"),
                       value = c(121.73567, 172.18096, 51.53028, 9.98687))
g5 = ggplot(outframe %>% filter(facility %in% gen_names, variable=="power", value>0), aes(x=value, fill=facility)) +
  geom_density(aes(y=..scaled..), alpha=0.5, color=NA) + xlim(0, NA) +
  geom_vline(data=g5.actual, aes(xintercept=value, color=facility)) +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank()) +
  labs(x="Power (MW)", y="Scaled density", fill="Facility")# +
  # ggsave('../_media/power_gens.png', width=24.7, height=10, units='cm')

ggplotly(g5, tooltip=c('facility', 'value'))

# tsgrob4.5 = grid_arrange_shared_legend(g4, g5, nrow=2, ncol=1, position = "right")
# ggsave('../_media/gens.png', tsgrob4.5, width=24.7, height=6, units='cm')

5.6 Gen Power

tb6 <- outframe %>% filter(variable=="sd") %>% select(facility, value) %>%
  mutate(well=factor(facility)) %>%
  group_by(well) %>%
  summarise(Mean = mean(value), 
            `Lower 2.5%` = quantile(value, 0.025), 
            `Upper 97.5%` = quantile(value, 0.975)) %>%
  mutate_if(is.numeric, round, 3) %>%
  inner_join(regression_df %>% mutate(well=factor(names(ids)[well_id])) %>% group_by(well) %>% summarise(n=n()), by="well")
g6 = ggplot(outframe %>% filter(variable=="sd") %>% filter(facility %in% special_wells), aes(x=value, fill=facility)) +
  geom_density(alpha=0.5, color=NA) + coord_cartesian(xlim=c(0, max(tb6$`Upper 97.5%`))) +
  theme(axis.text.y=element_blank(),
        axis.ticks.y=element_blank()) +
  labs(title="Posterior Flow Deviation Estimates", x="Standard deviation", y="Density", fill="Facility")# +
  # ggsave('../_media/standard_deviation.png', width=24.7, height=10, units='cm')
ggplotly(g6, tooltip=c('facility', 'value'))